API Traffic Calculator

Estimate your API usage costs, data transfer volumes, and performance impact with our comprehensive API Traffic Calculator. Understand the financial and operational implications of your API strategy before deployment.

API Usage & Cost Estimator

Key API Traffic Metrics Overview

Metric	Value	Unit	Description
Daily API Requests	0	requests	Your specified daily API call volume.
Monthly API Requests	0	requests	Total API calls over a 30-day period.
Monthly Data Transfer	0.00	GB	Total data moved (in/out) monthly.
Cost per 1,000 Requests	$0.00	$	Provider’s charge for API calls.
Data Transfer Cost per GB	$0.00	$	Provider’s charge for data movement.
Estimated Monthly Cost	$0.00	$	Total estimated cost for the month.

What is an API Traffic Calculator?

An API Traffic Calculator is a specialized tool designed to estimate the usage, performance, and associated costs of Application Programming Interfaces (APIs) based on projected traffic volumes. In today’s interconnected digital landscape, APIs are the backbone of most applications, enabling communication between different software systems. Understanding the implications of API usage—from the number of requests to the volume of data transferred—is crucial for budgeting, infrastructure planning, and maintaining service quality.

This calculator helps developers, product managers, and finance teams predict how their API consumption will translate into real-world expenses and operational load. It moves beyond simple request counts to factor in data sizes, latency, and concurrency, providing a holistic view of API resource utilization.

Who Should Use an API Traffic Calculator?

• Developers & Architects: To design scalable systems and choose appropriate API providers based on cost and performance.
• Product Managers: To forecast operational costs for new features or products that rely heavily on external APIs.
• Finance Teams: For accurate budgeting and cost control related to cloud services and third-party API integrations.
• DevOps & SRE Teams: To plan for infrastructure scaling, monitor API performance, and set realistic service level objectives (SLOs).
• Business Owners: To understand the financial viability of API-driven business models and optimize spending.

Common Misconceptions about API Traffic Calculation

Many assume API costs are solely based on the number of requests. However, this is a significant oversimplification. Key misconceptions include:

• Only Request Count Matters: While request count is a primary factor, data transfer volume (ingress and egress), storage, and specialized features (e.g., AI/ML API calls) often contribute significantly to the total cost.
• Fixed Costs: API pricing models are rarely fixed. They often involve tiered pricing, volume discounts, and separate charges for different operations or data regions.
• Ignoring Latency: High API latency can lead to poor user experience, increased resource consumption on the client side, and potentially higher infrastructure costs if more concurrent connections are needed to compensate.
• Underestimating Concurrency: Simply multiplying daily requests by 30 doesn’t account for peak loads. A sudden surge in concurrent requests can overwhelm systems, leading to errors and performance degradation, even if the daily average is manageable.
• Forgetting Hidden Fees: Some APIs have charges for failed requests, specific data formats, or premium support, which can add up.

API Traffic Calculator Formula and Mathematical Explanation

The API Traffic Calculator uses a set of interconnected formulas to provide a comprehensive estimate of API usage and its impact. These calculations help translate raw traffic numbers into actionable insights.

Step-by-Step Derivation:

1. Calculate Total Monthly API Requests:

   This is the most straightforward calculation, projecting daily usage over a standard month.

   Total Monthly API Requests = Expected Daily API Requests × 30

2. Calculate Total Monthly Data Transfer (GB):

   Data transfer costs are often a significant component of API usage, especially for data-intensive applications. This formula accounts for both the data sent (request) and received (response).

   Total Monthly Data Transfer (KB) = Expected Daily API Requests × (Average Request Size (KB) + Average Response Size (KB)) × 30

   To convert this to Gigabytes (GB), we divide by 1,048,576 (since 1 GB = 1024 MB = 1024 * 1024 KB).

   Total Monthly Data Transfer (GB) = Total Monthly Data Transfer (KB) / 1,048,576

3. Estimate Peak Concurrent Requests:

   While not a direct cost, understanding peak concurrency is vital for infrastructure sizing and preventing bottlenecks. The concurrency factor provides a simplified way to estimate this.

   Estimated Peak Concurrent Requests = Expected Daily API Requests × Concurrency Factor

4. Calculate Total Monthly Latency Impact (Hours):

   Latency directly impacts user experience and the efficiency of your application. This metric quantifies the cumulative delay introduced by API calls over a month.

   Total Monthly Latency Impact (ms) = Total Monthly API Requests × API Latency Per Request (ms)

   To convert this to hours, we divide by 3,600,000 (since 1 hour = 60 minutes = 3600 seconds = 3,600,000 milliseconds).

   Total Monthly Latency Impact (Hours) = Total Monthly Latency Impact (ms) / 3,600,000

5. Calculate Estimated Monthly API Cost:

   This is the final aggregation of all cost components, providing the total estimated expenditure.

   Estimated Monthly API Cost = (Total Monthly API Requests / 1000) × API Cost Per 1,000 Requests + (Total Monthly Data Transfer (GB) × Data Transfer Cost Per GB)

Variable Explanations:

API Traffic Calculator Variables

Variable	Meaning	Unit	Typical Range
Expected Daily API Requests	Average number of API calls per day.	requests/day	100 – 1,000,000+
Average Request Size (KB)	Size of data sent in each API request.	KB	0.1 – 100 KB
Average Response Size (KB)	Size of data received in each API response.	KB	0.1 – 1000 KB
API Cost Per 1,000 Requests	Cost charged by provider for 1,000 API calls.	$/1000 requests	$0.01 – $5.00
Data Transfer Cost Per GB	Cost charged for transferring 1 GB of data.	$/GB	$0.00 – $0.15
API Latency Per Request (ms)	Average time for one API call to complete.	ms	10 – 500 ms
Concurrency Factor	Proportion of daily requests concurrent at peak.	(ratio)	0.01 – 0.20

Practical Examples (Real-World Use Cases)

To illustrate the utility of the API Traffic Calculator, let’s consider two distinct scenarios:

Example 1: Small E-commerce Product Catalog API

Imagine a small e-commerce site that fetches product details from a third-party API. They expect moderate traffic.

• Expected Daily API Requests: 5,000 (e.g., 500 unique visitors, each viewing 10 products)
• Average Request Size (KB): 0.5 KB (simple product ID query)
• Average Response Size (KB): 20 KB (detailed product info with images URLs)
• API Cost Per 1,000 Requests: $0.20
• Data Transfer Cost Per GB: $0.08
• API Latency Per Request (ms): 80 ms
• Concurrency Factor: 0.02 (low peak concurrency)

Outputs:

• Total Monthly API Requests: 5,000 * 30 = 150,000 requests
• Total Monthly Data Transfer (GB): (5,000 * (0.5 + 20) * 30) / 1,048,576 = 29.37 GB
• Estimated Peak Concurrent Requests: 5,000 * 0.02 = 100 requests
• Total Monthly Latency Impact (Hours): (150,000 * 80) / 3,600,000 = 3.33 Hours
• Estimated Monthly API Cost: (150,000 / 1000) * $0.20 + (29.37 * $0.08) = $30.00 + $2.35 = $32.35

Interpretation: For a small e-commerce site, the API costs are very manageable. The data transfer cost is a minor component. The latency impact is also low, indicating good performance. This provides confidence in scaling this API integration.

Example 2: High-Traffic Mobile App with Real-time Data API

Consider a popular mobile weather app that fetches real-time weather data for millions of users.

• Expected Daily API Requests: 500,000 (e.g., 1 million users, each refreshing data once every two days)
• Average Request Size (KB): 0.2 KB (location coordinates)
• Average Response Size (KB): 5 KB (current weather, forecast)
• API Cost Per 1,000 Requests: $0.10 (volume discount)
• Data Transfer Cost Per GB: $0.09
• API Latency Per Request (ms): 150 ms (due to external data sources)
• Concurrency Factor: 0.10 (high peak usage during morning/evening)

Outputs:

• Total Monthly API Requests: 500,000 * 30 = 15,000,000 requests
• Total Monthly Data Transfer (GB): (500,000 * (0.2 + 5) * 30) / 1,048,576 = 743.8 GB
• Estimated Peak Concurrent Requests: 500,000 * 0.10 = 50,000 requests
• Total Monthly Latency Impact (Hours): (15,000,000 * 150) / 3,600,000 = 625 Hours
• Estimated Monthly API Cost: (15,000,000 / 1000) * $0.10 + (743.8 * $0.09) = $1,500.00 + $66.94 = $1,566.94

Interpretation: For a high-traffic app, the costs are substantial, primarily driven by the sheer volume of requests. Data transfer is also a noticeable cost. The high peak concurrent requests (50,000) indicate a need for robust infrastructure and potentially rate limiting strategies. The significant latency impact (625 hours) suggests that even small improvements in API latency could yield substantial overall performance gains for users. This API Traffic Calculator helps highlight these critical areas for optimization.

How to Use This API Traffic Calculator

Our API Traffic Calculator is designed for ease of use, providing quick and accurate estimates for your API usage. Follow these steps to get the most out of the tool:

Step-by-Step Instructions:

1. Input Expected Daily API Requests: Enter the average number of API calls you anticipate your application will make in a single day. This is the primary driver of your traffic.
2. Specify Average Request Size (KB): Provide the typical size (in Kilobytes) of the data payload your application sends with each API request. If your requests are simple (e.g., just an ID), this might be very small.
3. Specify Average Response Size (KB): Enter the typical size (in Kilobytes) of the data your application receives back from the API for each call. This can vary widely based on the API’s purpose.
4. Enter API Cost Per 1,000 Requests ($): Look up the pricing model of your API provider. This is usually a cost per thousand (or million) requests.
5. Input Data Transfer Cost Per GB ($): If your API provider or cloud infrastructure charges for data transfer (egress/ingress), enter that cost per Gigabyte. This is common for cloud services.
6. Provide API Latency Per Request (ms): Estimate the average time (in milliseconds) it takes for a single API call to complete, from sending the request to receiving the full response.
7. Set Concurrency Factor (0-1): This is a crucial input for performance planning. It represents the proportion of your daily requests that might occur simultaneously during peak periods. A factor of 0.05 means 5% of your daily requests could happen concurrently.
8. Click “Calculate API Traffic”: Once all fields are filled, click this button to generate your results. The calculator updates in real-time as you adjust inputs.
9. Click “Reset”: To clear all inputs and revert to default values, click the “Reset” button.
10. Click “Copy Results”: To easily share or save your calculation, click “Copy Results” to copy the key outputs to your clipboard.

How to Read Results:

• Estimated Monthly API Cost: This is your primary highlighted result, showing the total projected cost for API usage and data transfer over a month.
• Total Monthly API Requests: The total number of API calls expected in a 30-day period.
• Total Monthly Data Transfer (GB): The cumulative volume of data (request + response) transferred over a month, measured in Gigabytes.
• Estimated Peak Concurrent Requests: An important metric for infrastructure planning, indicating the maximum number of simultaneous API calls your system might need to handle.
• Total Monthly Latency Impact (Hours): The aggregate time spent waiting for API responses over a month. High values here indicate potential performance bottlenecks.

Decision-Making Guidance:

Use the results from this API Traffic Calculator to:

• Optimize Costs: Identify if API requests or data transfer are the primary cost drivers. Explore caching strategies, data compression, or alternative APIs to reduce expenses.
• Plan Infrastructure: The “Estimated Peak Concurrent Requests” helps you size your servers, load balancers, and database connections to handle peak loads without performance degradation.
• Improve Performance: A high “Total Monthly Latency Impact” suggests that even small reductions in API latency can significantly improve overall application responsiveness. Consider regional API endpoints or faster network connections.
• Negotiate Pricing: Armed with accurate usage projections, you can negotiate better pricing tiers or custom agreements with API providers.
• Set Monitoring Thresholds: Use the calculated values as baselines for your API monitoring tools to detect anomalies or unexpected spikes in usage.

Key Factors That Affect API Traffic Calculator Results

The accuracy and utility of the API Traffic Calculator depend heavily on the quality of your input data and your understanding of the underlying factors influencing API usage. Here are the key elements:

• Expected Daily API Requests (Traffic Volume): This is arguably the most significant factor. Higher daily requests directly increase monthly requests, data transfer, and ultimately, cost. Accurate forecasting of user activity, application features, and background processes that trigger API calls is crucial.
• Average Request & Response Sizes (Data Volume): Even with low request counts, large data payloads can quickly drive up data transfer costs. Optimizing data structures, using efficient serialization formats (like Protobuf instead of verbose JSON), and implementing pagination can significantly reduce these sizes.
• API Pricing Model (Cost Structure): Different APIs have varying pricing. Some charge per request, others per data volume, or a combination. Tiered pricing (e.g., first million requests free, then $X per 1000) or feature-specific charges (e.g., image recognition API costing more than a simple data retrieval API) will drastically alter the final cost.
• Data Transfer Costs (Network Egress/Ingress): Cloud providers often charge for data leaving their network (egress). If your API calls involve moving data across regions or out of a cloud provider’s network, these costs can accumulate rapidly, sometimes exceeding the API call costs themselves.
• API Latency (Performance Impact): While not a direct monetary cost, high latency impacts user experience, can lead to higher infrastructure costs (if connections are held open longer), and reduces the overall efficiency of your application. Factors like network distance, API server load, and database query times contribute to latency.
• Concurrency Patterns (Peak Load): The “Concurrency Factor” helps estimate peak load. Applications with sudden spikes in user activity (e.g., news apps during breaking news, e-commerce during flash sales) will have higher concurrency, requiring more robust infrastructure and potentially higher-tier API plans to avoid rate limiting or service degradation.
• Caching Strategies: Effective caching at various layers (client-side, CDN, server-side) can dramatically reduce the number of API requests and data transfer, thereby lowering costs and improving performance. The calculator assumes raw API calls; caching reduces the effective “Expected Daily API Requests.”
• Error Rates and Retries: If an API has a high error rate, your application might implement retry logic, leading to more API calls than initially intended. This increases both request count and latency impact. Robust error handling and monitoring are essential.

Frequently Asked Questions (FAQ)

Q: What is the primary benefit of using an API Traffic Calculator?

A: The primary benefit is gaining a clear, data-driven understanding of the financial and performance implications of your API usage. It helps in budgeting, infrastructure planning, and identifying areas for cost optimization and performance improvement before deployment.

Q: How accurate are the results from this API Traffic Calculator?

A: The accuracy of the API Traffic Calculator depends directly on the accuracy of your input values. If your estimates for daily requests, data sizes, and pricing are realistic, the results will be a very good approximation. It’s a projection tool, so real-world usage might vary slightly.

Q: Can this calculator account for tiered API pricing?

A: This specific calculator uses a flat “Cost Per 1,000 Requests” for simplicity. For complex tiered pricing (e.g., first 1M requests free, next 10M at $X, etc.), you would need to calculate the effective average cost per 1,000 requests based on your total projected volume and input that average into the calculator.

Q: What if my API has different costs for different types of requests?

A: If your API has varied costs (e.g., read operations vs. write operations), you should calculate a weighted average for “API Cost Per 1,000 Requests” and “Average Request/Response Size (KB)” based on the proportion of each request type you expect.

Q: Why is “Concurrency Factor” important for an API Traffic Calculator?

A: The “Concurrency Factor” helps estimate the peak load your system will experience. While daily averages are useful for total cost, peak concurrency dictates the required capacity of your servers, database connections, and API rate limits. Underestimating it can lead to performance issues or outages.

Q: Does the calculator consider API rate limits?

A: The calculator itself doesn’t directly enforce rate limits, but the “Estimated Peak Concurrent Requests” output can help you assess if your projected traffic will exceed an API’s rate limits. If it does, you’ll need to implement client-side rate limiting or request a higher limit from the API provider.

Q: How can I reduce my API costs based on the calculator’s results?

A: If costs are high, look at the primary drivers:

1. Reduce Requests: Implement caching, batching, or webhooks.
2. Reduce Data Transfer: Optimize data payloads, use compression, or pagination.
3. Negotiate Pricing: If your volume is high, discuss custom pricing with the provider.
4. Choose Cheaper APIs: Evaluate alternative APIs with more favorable pricing models.

Q: What are some limitations of this API Traffic Calculator?

A: This API Traffic Calculator provides a simplified model. It doesn’t account for:

• Complex tiered pricing structures.
• Specific API features with separate costs (e.g., AI processing, storage).
• Regional data transfer costs (which can vary).
• The cost of your own infrastructure to consume the API.
• Fluctuations in daily traffic (it uses an average).

It serves as an excellent starting point for estimation.

Related Tools and Internal Resources

Explore our other valuable resources to further optimize your API strategy and digital infrastructure:

• API Monitoring Tools Guide: Discover the best tools to track your API performance, uptime, and usage in real-time.
• Web Analytics API Guide: Learn how to integrate web analytics data into your applications using powerful APIs.
• Network Traffic Analysis Best Practices: Understand how to analyze and optimize network traffic for better application performance.
• API Security Best Practices: Secure your APIs against common threats and vulnerabilities with our expert guide.
• Understanding Cloud API Pricing Models: A deep dive into how major cloud providers charge for their API services.
• Understanding API Rate Limits: Learn about rate limiting strategies and how to manage them effectively for your applications.

// This is a simplified mock to satisfy the “no external libraries” rule while demonstrating chart functionality.
var Chart = function(ctx, config) {
this.ctx = ctx;
this.config = config;
this.data = config.data;
this.options = config.options;
this.chartType = config.type;
this.canvas = ctx.canvas;

this.draw = function() {
// Simplified drawing logic for demonstration
var width = this.canvas.width;
var height = this.canvas.height;
this.ctx.clearRect(0, 0, width, height);

var labels = this.data.labels;
var datasets = this.data.datasets;

// Basic scaling (very simplified)
var maxReq = 0;
var maxData = 0;
for (var i = 0; i < datasets[0].data.length; i++) { if (datasets[0].data[i] > maxReq) maxReq = datasets[0].data[i];
if (datasets[1].data[i] > maxData) maxData = datasets[1].data[i];
}

var padding = 50;
var xStep = (width – 2 * padding) / (labels.length – 1);

// Draw Y-axis for requests
this.ctx.strokeStyle = ‘#004a99’;
this.ctx.beginPath();
this.ctx.moveTo(padding, padding);
this.ctx.lineTo(padding, height – padding);
this.ctx.stroke();

// Draw Y-axis for data transfer
this.ctx.strokeStyle = ‘#28a745’;
this.ctx.beginPath();
this.ctx.moveTo(width – padding, padding);
this.ctx.lineTo(width – padding, height – padding);
this.ctx.stroke();

// Draw X-axis
this.ctx.strokeStyle = ‘#ccc’;
this.ctx.beginPath();
this.ctx.moveTo(padding, height – padding);
this.ctx.lineTo(width – padding, height – padding);
this.ctx.stroke();

// Draw data series
for (var d = 0; d < datasets.length; d++) { var dataset = datasets[d]; this.ctx.strokeStyle = dataset.borderColor; this.ctx.fillStyle = dataset.backgroundColor; this.ctx.lineWidth = dataset.borderWidth; this.ctx.beginPath(); for (var i = 0; i < dataset.data.length; i++) { var x = padding + i * xStep; var y; if (dataset.yAxisID === 'y-axis-requests') { y = height - padding - (dataset.data[i] / maxReq) * (height - 2 * padding); } else { y = height - padding - (dataset.data[i] / maxData) * (height - 2 * padding); } if (i === 0) { this.ctx.moveTo(x, y); } else { this.ctx.lineTo(x, y); } } this.ctx.stroke(); } // Add labels (very basic) this.ctx.fillStyle = '#333'; this.ctx.font = '10px Arial'; for (var i = 0; i < labels.length; i++) { var x = padding + i * xStep; this.ctx.fillText(labels[i], x - 10, height - padding + 20); } this.ctx.fillText(this.options.scales.x.title.text, width / 2 - 50, height - 10); this.ctx.fillText(this.options.scales['y-axis-requests'].title.text, 10, height / 2); this.ctx.fillText(this.options.scales['y-axis-data'].title.text, width - 40, height / 2); }; this.update = function() { this.draw(); }; this.destroy = function() { // Clear canvas this.ctx.clearRect(0, 0, this.canvas.width, this.canvas.height); }; this.draw(); // Initial draw }; function validateInput(id) { var inputElement = document.getElementById(id); var errorElement = document.getElementById(id + 'Error'); var value = parseFloat(inputElement.value); var min = parseFloat(inputElement.min); var max = parseFloat(inputElement.max); errorElement.textContent = ''; // Clear previous error if (inputElement.value.trim() === '') { errorElement.textContent = 'This field cannot be empty.'; return false; } if (isNaN(value)) { errorElement.textContent = 'Please enter a valid number.'; return false; } if (value < min) { errorElement.textContent = 'Value cannot be less than ' + min + '.'; return false; } if (id === 'concurrencyFactor' && value > max) {
errorElement.textContent = ‘Value cannot be greater than ‘ + max + ‘.’;
return false;
}
return true;
}

function calculateApiTraffic() {
var isValid = true;
var inputs = [
‘dailyApiRequests’, ‘avgRequestSizeKB’, ‘avgResponseSizeKB’,
‘costPer1000Requests’, ‘dataTransferCostPerGB’, ‘apiLatencyMS’,
‘concurrencyFactor’
];

for (var i = 0; i < inputs.length; i++) { if (!validateInput(inputs[i])) { isValid = false; } } if (!isValid) { document.getElementById('estimatedMonthlyApiCost').textContent = '$0.00'; document.getElementById('totalMonthlyApiRequests').textContent = '0'; document.getElementById('totalMonthlyDataTransferGB').textContent = '0.00 GB'; document.getElementById('peakConcurrentRequests').textContent = '0'; document.getElementById('totalMonthlyLatencyImpactHours').textContent = '0.00 Hours'; updateTable(0, 0, 0, 0, 0, 0); drawChart(0, 0); // Clear chart or show default return; } var dailyApiRequests = parseFloat(document.getElementById('dailyApiRequests').value); var avgRequestSizeKB = parseFloat(document.getElementById('avgRequestSizeKB').value); var avgResponseSizeKB = parseFloat(document.getElementById('avgResponseSizeKB').value); var costPer1000Requests = parseFloat(document.getElementById('costPer1000Requests').value); var dataTransferCostPerGB = parseFloat(document.getElementById('dataTransferCostPerGB').value); var apiLatencyMS = parseFloat(document.getElementById('apiLatencyMS').value); var concurrencyFactor = parseFloat(document.getElementById('concurrencyFactor').value); // Calculations var totalMonthlyApiRequests = dailyApiRequests * 30; var totalMonthlyDataTransferKB = dailyApiRequests * (avgRequestSizeKB + avgResponseSizeKB) * 30; var totalMonthlyDataTransferGB = totalMonthlyDataTransferKB / 1048576; // 1 GB = 1024 * 1024 KB var estimatedMonthlyApiCost = (totalMonthlyApiRequests / 1000) * costPer1000Requests + (totalMonthlyDataTransferGB * dataTransferCostPerGB); var peakConcurrentRequests = dailyApiRequests * concurrencyFactor; var totalMonthlyLatencyImpactMS = totalMonthlyApiRequests * apiLatencyMS; var totalMonthlyLatencyImpactHours = totalMonthlyLatencyImpactMS / 3600000; // 1 hour = 3,600,000 ms // Display results document.getElementById('estimatedMonthlyApiCost').textContent = '$' + estimatedMonthlyApiCost.toFixed(2); document.getElementById('totalMonthlyApiRequests').textContent = totalMonthlyApiRequests.toLocaleString(); document.getElementById('totalMonthlyDataTransferGB').textContent = totalMonthlyDataTransferGB.toFixed(2) + ' GB'; document.getElementById('peakConcurrentRequests').textContent = Math.round(peakConcurrentRequests).toLocaleString(); document.getElementById('totalMonthlyLatencyImpactHours').textContent = totalMonthlyLatencyImpactHours.toFixed(2) + ' Hours'; updateTable( dailyApiRequests, totalMonthlyApiRequests, totalMonthlyDataTransferGB, costPer1000Requests, dataTransferCostPerGB, estimatedMonthlyApiCost ); drawChart(totalMonthlyApiRequests, totalMonthlyDataTransferGB); } function updateTable(dailyReq, monthlyReq, monthlyData, costPer1k, dataCostPerGB, totalCost) { document.getElementById('tableDailyApiRequests').textContent = dailyReq.toLocaleString(); document.getElementById('tableMonthlyApiRequests').textContent = monthlyReq.toLocaleString(); document.getElementById('tableMonthlyDataTransferGB').textContent = monthlyData.toFixed(2); document.getElementById('tableCostPer1000Requests').textContent = '$' + costPer1k.toFixed(2); document.getElementById('tableDataTransferCostPerGB').textContent = '$' + dataCostPerGB.toFixed(2); document.getElementById('tableEstimatedMonthlyApiCost').textContent = '$' + totalCost.toFixed(2); } function resetCalculator() { document.getElementById('dailyApiRequests').value = '10000'; document.getElementById('avgRequestSizeKB').value = '1'; document.getElementById('avgResponseSizeKB').value = '5'; document.getElementById('costPer1000Requests').value = '0.50'; document.getElementById('dataTransferCostPerGB').value = '0.09'; document.getElementById('apiLatencyMS').value = '50'; document.getElementById('concurrencyFactor').value = '0.05'; // Clear error messages var errorElements = document.querySelectorAll('.error-message'); for (var i = 0; i < errorElements.length; i++) { errorElements[i].textContent = ''; } calculateApiTraffic(); // Recalculate with default values } function copyResults() { var estimatedMonthlyApiCost = document.getElementById('estimatedMonthlyApiCost').textContent; var totalMonthlyApiRequests = document.getElementById('totalMonthlyApiRequests').textContent; var totalMonthlyDataTransferGB = document.getElementById('totalMonthlyDataTransferGB').textContent; var peakConcurrentRequests = document.getElementById('peakConcurrentRequests').textContent; var totalMonthlyLatencyImpactHours = document.getElementById('totalMonthlyLatencyImpactHours').textContent; var dailyApiRequests = document.getElementById('dailyApiRequests').value; var avgRequestSizeKB = document.getElementById('avgRequestSizeKB').value; var avgResponseSizeKB = document.getElementById('avgResponseSizeKB').value; var costPer1000Requests = document.getElementById('costPer1000Requests').value; var dataTransferCostPerGB = document.getElementById('dataTransferCostPerGB').value; var apiLatencyMS = document.getElementById('apiLatencyMS').value; var concurrencyFactor = document.getElementById('concurrencyFactor').value; var resultsText = "API Traffic Calculator Results:\n\n" + "Estimated Monthly API Cost: " + estimatedMonthlyApiCost + "\n" + "Total Monthly API Requests: " + totalMonthlyApiRequests + "\n" + "Total Monthly Data Transfer: " + totalMonthlyDataTransferGB + "\n" + "Estimated Peak Concurrent Requests: " + peakConcurrentRequests + "\n" + "Total Monthly Latency Impact: " + totalMonthlyLatencyImpactHours + "\n\n" + "Key Assumptions:\n" + "Daily API Requests: " + dailyApiRequests + "\n" + "Avg Request Size (KB): " + avgRequestSizeKB + "\n" + "Avg Response Size (KB): " + avgResponseSizeKB + "\n" + "Cost Per 1,000 Requests: $" + costPer1000Requests + "\n" + "Data Transfer Cost Per GB: $" + dataTransferCostPerGB + "\n" + "API Latency Per Request (ms): " + apiLatencyMS + "\n" + "Concurrency Factor: " + concurrencyFactor; navigator.clipboard.writeText(resultsText).then(function() { alert('Results copied to clipboard!'); }).catch(function(err) { console.error('Could not copy text: ', err); alert('Failed to copy results. Please try again or copy manually.'); }); } // Initialize calculator on page load window.onload = function() { calculateApiTraffic(); };